Triple
T3458639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karelian language |
E72966
|
entity |
| Predicate | numberOfCasesApproximate |
P11610
|
FINISHED |
| Object | 14 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 14 | Statement: [Karelian language, numberOfCasesApproximate, 14]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCasesApproximate Context triple: [Karelian language, numberOfCasesApproximate, 14]
-
A.
numberOfCases
chosen
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
B.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
C.
typeOfCasesHandled
Indicates the categories or kinds of cases that an entity (such as a person, organization, or system) is responsible for managing or processing.
-
D.
defendantCount
Indicates the number of defendants involved in a particular legal case or proceeding.
-
E.
numberOfIndictedPersonsApproximate
Indicates an approximate count of persons who have been formally indicted in a given context or case.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ad85b12a908190a1d10a6b03b4f8ae |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbae4c18881908b48d16e46f78209 |
completed | March 8, 2026, 6:07 p.m. |
| PD | Predicate disambiguation | batch_69adae041d588190a84a02bca94adec8 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:16 p.m.